Advances and visions in large-scale hydrological modelling: findings from the 11th Workshop on Large-Scale Hydrological Modelling

Abstract

Abstract. Large-scale hydrological modelling has become increasingly wide-spread during the last decade. An annual workshop series on large-scale hydrological modelling has provided, since 1997, a forum to the German-speaking community for discussing recent developments and achievements in this research area. In this paper we present the findings from the 2007 workshop which focused on advances and visions in large-scale hydrological modelling. We identify the state of the art, difficulties and research perspectives with respect to the themes "sensitivity of model results", "integrated modelling" and "coupling of processes in hydrosphere, atmosphere and biosphere". Some achievements in large-scale hydrological modelling during the last ten years are presented together with a selection of remaining challenges for the future.

Effects of land cover change on flood peak discharges and runoff volumes: model estimates for the Nyando River Basin, Kenya

“…As a result, such models are extensively utilized for studies of the effects of land cover changes on surface and sub‐surface hydrologic processes (Xu and Singh, 2004; McColl and Aggett, 2007; Saghafian et al , 2007). In hydrological simulation studies, the selection of appropriate spatial and temporal scales to represent a hydrological variable of consideration is essential to capture the relevant random and non‐random patterns (Grayson and Blöschl, 2000; Cerdan et al , 2004; Doll et al , 2008). A careful evaluation of the hydrological attribute s versus the quality of the existing datasets is hence important.…”

Macroscale hydrological modelling approach for study of large scale hydrologic impacts under climate change in Indian river basins

“…It is well recognized that model calibration to river discharge at the basin outlet does not guarantee that other elements of the water cycle are represented well in their spatial heterogeneity. By adjusting a large number of parameters, it might be possible to obtain a good fit even if the model structure is not appropriate, and calibration may even prevent an improved system understanding (Döll et al, 2008). The 20C3M biases seen in Figure 8 for surface runoff are of the same order of magnitude as projected runoff changes for the future as shown in Figure 11.…”

Large‐scale hydrology: advances in understanding processes, dynamics and models from beyond river basin to global scale

“…Gosling & Arnell, 2011), and increasingly to catchment hydrology models run at large scales (Pappenberger et al , 2011). The methods and drivers for improving prediction are necessarily varied for each modelling approach, as are the challenges faced (Döll et al , 2008). In all the cases, improving predictions relies on a series of large‐scale hydrological data sets to drive the models and to evaluate predictions, with several of the most recent and high‐quality sets represented in the articles in this Special Issue [e.g.…”

“…Given increasing concern about climate change and human impacts on the hydrological cycle and water resources, it is critical to provide information on current and future hydrological dynamics and process understanding over large spatial domains (Döll, 2009; World Water Assessment Programme, 2009; Hannah et al , 2011). This knowledge is central to improving large‐scale hydrological modelling approaches and predictions (Yang & Musiake, 2003; Bordi et al , 2009; Dankers & Feyen, 2008; Döll et al , 2008; Döll & Fiedler, 2008; Hagemann et al , 2008; Pappenberger et al , 2008; Getirana et al , 2010). In particular, it is important to conduct research that seeks to elucidate patterns and drivers of widespread hydrological response, to identify those regions and time periods most susceptible to climate change/variability and anthropogenic influences, and to use scientific findings to inform decision making so that water hazards and stress (e.g.…”